摘要
生物医学因果关系抽取是BioCreative社区提出的一项评测任务,旨在挖掘生物医学实体间丰富的语义关系,并用生物医学表征语言(biological expression language, BEL)来表示。与传统的实体关系抽取不同,该任务不仅包含实体间因果关系的抽取,还包含实体功能的识别。此前已经提出了一些该任务的解决方法,但均未考虑这两个子任务间的关联性。该文基于多任务的思想,提出一种二元关系抽取和一元功能识别共同决策的联合学习模式。首先两个任务共享底层向量表示,然后利用长短期记忆(long short-term memory, LSTM)网络和门控机制学习两个任务之间的交互表示,最后分别进行分类预测。实验结果表明,该方法能够融合两个子任务的信息,在2015 BC-V测试集上获得了45.3%的F值。
Biomedical causality extraction is an evaluation task proposed by the BioCreative community to explore the rich semantic relationships between biomedical entities. Unlike traditional entity relation extraction focusing only on binary relationships, this task includes the identification of function acting on one or more entities. Based on the idea of multi-task learning, a joint learning model sharing decision-making by both binary relation extraction and unary function detection is proposed. On the shared word embeddings, LSTM with gated mechanism are employed to learn the interactive representation between two tasks, and the final predictions are performed respectively. The experimental results show that this method can exploit the information of two tasks, achieving 45.3% F-score on the 2015 BC-V dataset.
作者
刘苏文
邵一帆
钱龙华
LIU Suwen;SHAO Yifan;QIAN Longhua(School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)
出处
《中文信息学报》
CSCD
北大核心
2020年第4期60-68,共9页
Journal of Chinese Information Processing
基金
国家自然科学基金(2017YFB1002101,61976147)
关键词
因果关系抽取
联合学习
门控机制
causality relation extraction
joint learning
gated mechanism
作者简介
刘苏文(1994—),硕士研究生,主要研究领域为信息抽取。E-mail:20174227016@stu.suda.edu.cn;邵一帆(1996—),硕士研究生,主要研究领域为信息抽取。E-mail:20185227012@stu.suda.edu.cn;通信作者:钱龙华(1966—),教授,硕士生导师,主要研究领域为自然语言处理。E-mail:qianlonghua@suda.edu.cn
